We comprehensively compare our FGDCN-S and FGDCN-L with recent state-of-the-art methods, including DAIN [1]}, CAIN [2]}, AdaCof [3]}, SoftSplat [4]}, RIFE [5]}, ABME [6]}, VFIformer-Small [7]} and IFRNet-Large [8]}. The quantitative results of these methods are shown in Table REF .All the reported results are copied from their original papers. The bolded and underlined data represent the best and second-best results, respectively.
It can be observed that our FGDCN-L outperforms recent state-of-the-art methods on all four testing sets with fewer parameters. Our FGDCN-S has the fewest parameters, but it achieves competitive performance compared with recent state-of-the-art methods. We also compare the running times of recent state-of-the-art methods in Table REF . The running time is tested on images with \(448\times 256\)  resolution on an NVIDIA RTX3090 GPU. Our FGDCN-L runs \(\times 3\)  faster than VFIformer-S and slightly slower than IFRNet-L. FGDCN-S is the fastest one among these methods. These experiments fully demonstrate the high efficiency of our FGDCN.
